SMS Management System for Direct Sales and Network Marketing

SMS management system in direct sales and network marketing is a concept of integrating information system with mobile phone as well as using short message service (SMS) as a medium of communication in the business process of direct sales and network marketing sector. Direct sales and network marketing sector is a business phenomenon which expanding rapidly within these few years and it will keep on expanding. To deal with the large members and distributors joining the company, the management of these companies started to seek for new direction in upgrading the relationship management between the company and the distributors. This is important when the low cost and time saving SMS is introduce to these direct selling companies. With the intention of enhancing the connection between distributors is an opportunity to integrate SMS system in the management system in this industry. In this paper, we have analyzed how the SMS will play an important role in the business process by allowing the end user and the company will benefit from its simple and cost saving

Investigating The Relationship Between Adverse Events And Infrastructure Development In An Active War Theater Using Soft Computing Techniques

The military recently recognized the importance of taking sociocultural factors into consideration. Therefore, Human Social Culture Behavior (HSCB) modeling has been getting much attention in current and future operational requirements to successfully understand the effects of social and cultural factors on human behavior. There are different kinds of modeling approaches to the data that are being used in this field and so far none of them has been widely accepted. HSCB modeling needs the capability to represent complex, ill-defined, and imprecise concepts, and soft computing modeling can deal with these concepts. There is currently no study on the use of any computational methodology for representing the relationship between adverse events and infrastructure development investments in an active war theater. This study investigates the relationship between adverse events and infrastructure development projects in an active war theater using soft computing techniques including fuzzy inference systems (FIS), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFIS) that directly benefits from their accuracy in prediction applications. Fourteen developmental and economic improvement project types were selected based on allocated budget values and a number of projects at different time periods, urban and rural population density, and total adverse event numbers at previous month selected as independent variables. A total of four outputs reflecting the adverse events in terms of the number of people killed, wounded, hijacked, and total number of adverse events has been estimated. For each model, the data was grouped for training and testing as follows: years between 2004 and 2009 (for training purpose) and year 2010 (for testing). Ninety-six different models were developed and investigated for Afghanistan iv and the country was divided into seven regions for analysis purposes. Performance of each model was investigated and compared to all other models with the calculated mean absolute error (MAE) values and the prediction accuracy within ±1 error range (difference between actual and predicted value). Furthermore, sensitivity analysis was performed to determine the effects of input values on dependent variables and to rank the top ten input parameters in order of importance. According to the the results obtained, it was concluded that the ANNs, FIS, and ANFIS are useful modeling techniques for predicting the number of adverse events based on historical development or economic projects’ data. When the model accuracy was calculated based on the MAE for each of the models, the ANN had better predictive accuracy than FIS and ANFIS models in general as demonstrated by experimental results. The percentages of prediction accuracy with values found within ±1 error range around 90%. The sensitivity analysis results show that the importance of economic development projects varies based on the regions, population density, and occurrence of adverse events in Afghanistan. For the purpose of allocating resources and development of regions, the results can be summarized by examining the relationship between adverse events and infrastructure development in an active war theater; emphasis was on predicting the occurrence of events and assessing the potential impact of regional infrastructure development efforts on reducing number of such events

Effet de la composition des matériaux composites sur la caractérisation et détection par ondes de Lamb

Les matériaux composites sont de plus en plus utilisés en aéronautique. Leurs excellentes propriétés mécaniques et leur faible poids leur procurent un avantage certain par rapport aux matériaux métalliques. Ceux-ci étant soumis à diverses conditions de chargement et environnementales, ils sont susceptibles de subir plusieurs types d'endommagements, compromettant leur intégrité. Des méthodes fiables d'inspection sont donc nécessaires pour évaluer leur intégrité. Néanmoins, peu d'approches non destructives, embarquées et efficaces sont présentement utilisées. Ce travail de recherche se penche sur l'étude de l'effet de la composition des matériaux composites sur la détection et la caractérisation par ondes guidées. L'objectif du projet est de développer une approche de caractérisation mécanique embarquée permettant d'améliorer la performance d'une approche d'imagerie par antenne piézoélectrique sur des structures composites et métalliques. La contribution de ce projet est de proposer une approche embarquée de caractérisation mécanique par ultrasons qui ne requiert pas une mesure sur une multitude d'échantillons et qui est non destructive. Ce mémoire par articles est divisé en quatre parties, dont les parties deux à quatre présentent les articles publiés et soumis. La première partie présente l'état des connaissances dans la matière nécessaire à l'accomplissement de ce projet de maitrise.Les principaux sujets traités portent sur les matériaux composites, propagation d'ondes, la modélisation des ondes guidées, la caractérisation par ondes guidées et la surveillance embarquée des structures. La deuxième partie présente une étude de l'effet des propriétés mécaniques sur la performance de l'algorithme d'imagerie Excitelet. L'étude est faite sur une structure isotrope.Les résultats ont démontré que l'algorithme est sensible à l'exactitude des propriétés mécaniques utilisées dans le modèle. Cette sensibilité a également été explorée afin de développer une méthode embarquée permettant d'évaluer les propriétés mécaniques d'une structure. La troisième partie porte sur une étude plus rigoureuse des performances de la méthode de caractérisation mécanique embarquée. La précision, la répétabilité et la robustesse de la méthode sont validés à l'aide d'un simulateur par FEM.Les propriétés estimées avec l'approche de caractérisation sont à moins de 1% des propriétés utilisées dans le modèle, ce qui rivalise avec l'incertitude des méthodes ASTM. L'analyse expérimentale s'est avérée précise et répétable pour des fréquences sous les 200 kHz, permettant d'estimer les propriétés mécaniques à moins de 1% des propriétés du fournisseur. La quatrième partie a démontré la capacité de l'approche de caractérisation à identifier les propriétés mécaniques d'une plaque composite orthotrope.Les résultats estimés expérimentalement sont inclus dans les barres d'incertitude des propriétés estimées à l'aide des tests ASTM. Finalement, une simulation FEM a démontré la précision de l'approche avec des propriétés mécaniques à moins de 4 % des propriétés du modèle simulé

Learning classifier systems from first principles: A probabilistic reformulation of learning classifier systems from the perspective of machine learning

Learning Classifier Systems (LCS) are a family of rule-based machine learning methods. They aim at the autonomous production of potentially human readable results that are the most compact generalised representation whilst also maintaining high predictive accuracy, with a wide range of application areas, such as autonomous robotics, economics, and multi-agent systems. Their design is mainly approached heuristically and, even though their performance is competitive in regression and classification tasks, they do not meet their expected performance in sequential decision tasks despite being initially designed for such tasks. It is out contention that improvement is hindered by a lack of theoretical understanding of their underlying mechanisms and dynamics.EThOS - Electronic Theses Online ServiceGBUnited Kingdo